This package contains a function for generating conditional survival estimates with associated confidence intervals, and a function for plotting conditional survival curves.

Install the package using

```
remotes::install_github("zabore/condsurv")
```

If (S(t)) represents the survival function at time (t), then conditional survival is defined as

[S(y|x) = \frac{S(x + y)}{S(x)}]

where (y) is the number of additional survival years of interest and (x) is the number of years a subject has already survived.

The `conditional_surv_est`

function will generate this estimate along
with 95% confidence intervals.

The `lung`

dataset from the `survival`

package will be used to
illustrate.

```
library(survival)
library(dplyr)
# Scale the time variable to be in years rather than days
lung2 <-
mutate(
lung,
os_yrs = time / 365.25
)
```

First generate a single conditional survival estimate. This is the
conditional survival of surviving to 1 year conditioned on already
having survived 6 months. This returns a list, where `cs_est`

is the
conditional survival estimate, `cs_lci`

is the lower bound of the 95%
confidence interval and `cs_uci`

is the upper bound of the 95%
confidence interval.

```
library(condsurv)
myfit <- survfit(Surv(os_yrs, status) ~ 1, data = lung2)
conditional_surv_est(
basekm = myfit,
t1 = 0.5,
t2 = 1
)
```

```
## $cs_est
## [1] 0.58
##
## $cs_lci
## [1] 0.49
##
## $cs_uci
## [1] 0.66
```

You can easily use `purrr::map_df`

to get a table of estimates for
multiple timepoints. For example we could get the conditional survival
estimate of surviving to a variety of different time points given that
the subject has already survived for 6 months (0.5 years).

```
prob_times <- seq(1, 2.5, 0.5)
purrr::map_df(
prob_times,
~conditional_surv_est(
basekm = myfit,
t1 = 0.5,
t2 = .x)
) %>%
mutate(years = prob_times) %>%
select(years, everything()) %>%
knitr::kable()
```

| years | cs_est | cs_lci | cs_uci | | ----: | ------: | ------: | ------: | | 1.0 | 0.58 | 0.49 | 0.66 | | 1.5 | 0.36 | 0.27 | 0.45 | | 2.0 | 0.16 | 0.09 | 0.24 | | 2.5 | 0.07 | 0.01 | 0.13 |

To plot the conditional survival curves at baseline, and for those who
have survived 6 months, 1 year, 1.5 years, and 2 years, we use the
`gg_conditional_surv`

function.

```
cond_times <- seq(0, 2, 0.5)
gg_conditional_surv(
basekm = myfit,
at = cond_times,
main = "Conditional survival in lung data"
)
```

zabore/condsurv documentation built on May 4, 2019, 8:47 p.m.

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